





'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			Optional[Any]						)->	str:
        snake_case 						=   False



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Tuple   ,       __snake_case							:			Tuple   ,       __snake_case							:			int						)->	str:
        if not self.initialized:
           snake_case 						=   RagRetriever(
               __snake_case   ,       question_encoder_tokenizer=__snake_case   ,       generator_tokenizer=__snake_case   ,       index=__snake_case   ,       init_retrieval=__snake_case   ,       )
           snake_case 						=   True



     def      lowerCAmelCase			(   self							:			Tuple						)->	Any:
        self.retriever.index.init_index()



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			int						)->	str:
        snake_case						,    snake_case 						=   self.retriever._main_retrieve(__snake_case   ,       __snake_case						)
        return doc_ids, retrieved_doc_embeds




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __init__(   self							:			List[str]   ,       __snake_case							:			Tuple   ,       __snake_case							:			Dict   ,       __snake_case							:			Any   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Dict=None						)->	Optional[int]:
        if index is not None and index.is_initialized() and len(__snake_case						) > 0:
           raise ValueError(
               """When using Ray for distributed fine-tuning, """
               """you\'ll need to provide the paths instead, """
               """as the dataset and the index are loaded """
               """separately. More info in examples/rag/use_own_knowledge_dataset.py """						)
        super().__init__(
            __snake_case   ,       question_encoder_tokenizer=__snake_case   ,       generator_tokenizer=__snake_case   ,       index=__snake_case   ,       init_retrieval=__snake_case   ,       )
        snake_case 						=   retrieval_workers
        if len(self.retrieval_workers						) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)
                   for worker in self.retrieval_workers
               ]						)



     def      lowerCAmelCase			(   self							:			Tuple						)->	Optional[Any]:
        logger.info("""initializing retrieval"""						)

        if len(self.retrieval_workers						) > 0:
           ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]						)
        else:
           # Non-distributed training. Load index into this same process.
           self.index.init_index()



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			List[Any]   ,       __snake_case							:			List[str]						)->	Dict:
        if len(self.retrieval_workers						) > 0:
           # Select a random retrieval actor.
           snake_case 						=   self.retrieval_workers[random.randint(0   ,       len(self.retrieval_workers						) - 1						)]
           snake_case						,    snake_case 						=   ray.get(random_worker.retrieve.remote(__snake_case   ,       __snake_case						)						)
        else:
           snake_case						,    snake_case 						=   self._main_retrieve(__snake_case   ,       __snake_case						)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__snake_case						)



     @classmethod
     def      lowerCAmelCase			(   cls							:			List[str]   ,       __snake_case							:			Dict   ,       __snake_case							:			Union[str, Any]=None   ,       **__snake_case							:			Optional[Any]						)->	Union[str, Any]:
        return super(__snake_case   ,       cls						).get_tokenizers(__snake_case   ,       __snake_case   ,       **__snake_case						)



     @classmethod
     def      lowerCAmelCase			(   cls							:			Optional[int]   ,       __snake_case							:			Dict   ,       __snake_case							:			Tuple   ,       __snake_case							:			str=None   ,       **__snake_case							:			Any						)->	Optional[Any]:
        snake_case 						=   kwargs.pop("""config"""   ,       __snake_case						) or RagConfig.from_pretrained(__snake_case   ,       **__snake_case						)
        snake_case 						=   RagTokenizer.from_pretrained(__snake_case   ,       config=__snake_case						)
        snake_case 						=   rag_tokenizer.question_encoder
        snake_case 						=   rag_tokenizer.generator
        if indexed_dataset is not None:
           snake_case 						=   """custom"""
           snake_case 						=   CustomHFIndex(config.retrieval_vector_size   ,       __snake_case						)
        else:
           snake_case 						=   cls._build_index(__snake_case						)
        return cls(
            __snake_case   ,       question_encoder_tokenizer=__snake_case   ,       generator_tokenizer=__snake_case   ,       retrieval_workers=__snake_case   ,       index=__snake_case   ,       )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ClapModel",
      "ClapPreTrainedModel",
      "ClapTextModel",
      "ClapTextModelWithProjection",
      "ClapAudioModel",
      "ClapAudioModelWithProjection",
  ]
  UpperCAmelCase_					=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioConfig,
      ClapConfig,
      ClapTextConfig,
  )
  from .processing_clap import ClapProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clap import ClapFeatureExtractor
    from .modeling_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapPreTrainedModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



